Strategies to connect RDF Graphs for Link Prediction using Drug-Disease Knowledge Graphs

<div>Traditionally, drug development is a time-consuming and</div><div>costly process. Using the vast amount of available data, it is hoped that new information can be mined or inferred automatically, reducing this cost. In this work, we present steps towards completing the ReDrugS KB, which others have used to predict interactions between various drugs and</div><div>diseases. Our goal is to further complete this graph, without human intervention in the process, aiming at a high recall. For the link prediction, we used state-of-the-art embedding techniques for RDF graphs. The embeddings are fed into binary classi fiers which predict the relation existence</div><div>between entities. The ReDrugS knowledge graph is the combination of the results of many studies organised in 8 million named graphs. These graphs are not entirely disjoint and might even contain contradictory assertions. Hence, a signi cant challenge is making the named graphs suitable for graph embedding, by combining them into a single graph. In</div><div>this work, we report on classi fication performance on the merged KG.</div>